{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo shows how to use integrated gradients in graph convolutional networks to obtain accurate importance estimations for both the nodes and edges. The notebook consists of three parts:\n",
    "- setting up the node classification problem for Cora citation network\n",
    "- training and evaluating a GCN model for node classification\n",
    "- calculating node and edge importances for model's predictions of query (\"target\") nodes\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Axiomatic Attribution for Deep Networks. M. Sundararajan, A. Taly, and Q. Yan.\n",
    "    Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017\n",
    "    ([link](https://arxiv.org/pdf/1703.01365.pdf)).\n",
    "    \n",
    "[2] Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. arXiv: 1903.01610 ([link](https://arxiv.org/abs/1903.01610))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "import time\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, regularizers\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")\n",
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")\n",
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The adjacency matrix is ordered by the graph nodes from G.nodes(). To make computations easier, let's reindex the node data to maintain the same ordering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_nodes = list(Gnx.nodes())\n",
    "node_data = node_data.loc[graph_nodes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=140, test_size=None, stratify=node_data[\"subject\"]\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data, train_size=500, test_size=None, stratify=test_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ...\n",
    "\n",
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))\n",
    "\n",
    "node_ids = node_data.index\n",
    "all_targets = target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\"))\n",
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the GCN model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. To feed data from the graph to the Keras model we need a generator. Since GCN is a full-batch model, we use the `FullBatchNodeGenerator` class.\n",
    "\n",
    "Note: For interpretability we require a dense matrix so we set `sparse=False` in the `FullBatchNodeGenerator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GCN (local pooling) filters...\n"
     ]
    }
   ],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)\n",
    "generator = FullBatchNodeGenerator(G, sparse=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model: tn this example we use two GCN layers with 16-dimensional hidden node features at each layer with ELU activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [16, 16]\n",
    "gcn = GCN(\n",
    "    layer_sizes=layer_sizes,\n",
    "    activations=[\"elu\", \"elu\"],\n",
    "    generator=generator,\n",
    "    dropout=0.3,\n",
    "    kernel_regularizer=regularizers.l2(5e-4),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expose the input and output tensors of the GCN model for node prediction, via GCN.build() method:\n",
    "x_inp, x_out = gcn.build()\n",
    "# Snap the final estimator layer to x_out\n",
    "x_out = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `x_out` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=x_out)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),  # decay=0.001),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[metrics.categorical_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 - 0s - loss: 1.9638 - categorical_accuracy: 0.1500 - val_loss: 1.8206 - val_categorical_accuracy: 0.3540\n",
      "Epoch 2/20\n",
      "1/1 - 0s - loss: 1.7804 - categorical_accuracy: 0.4071 - val_loss: 1.7096 - val_categorical_accuracy: 0.3320\n",
      "Epoch 3/20\n",
      "1/1 - 0s - loss: 1.6182 - categorical_accuracy: 0.4071 - val_loss: 1.6092 - val_categorical_accuracy: 0.3580\n",
      "Epoch 4/20\n",
      "1/1 - 0s - loss: 1.4788 - categorical_accuracy: 0.4857 - val_loss: 1.5087 - val_categorical_accuracy: 0.4360\n",
      "Epoch 5/20\n",
      "1/1 - 0s - loss: 1.3329 - categorical_accuracy: 0.6286 - val_loss: 1.4006 - val_categorical_accuracy: 0.5640\n",
      "Epoch 6/20\n",
      "1/1 - 0s - loss: 1.1915 - categorical_accuracy: 0.7357 - val_loss: 1.2882 - val_categorical_accuracy: 0.6680\n",
      "Epoch 7/20\n",
      "1/1 - 0s - loss: 1.0215 - categorical_accuracy: 0.8214 - val_loss: 1.1801 - val_categorical_accuracy: 0.7380\n",
      "Epoch 8/20\n",
      "1/1 - 0s - loss: 0.8702 - categorical_accuracy: 0.9286 - val_loss: 1.0839 - val_categorical_accuracy: 0.7780\n",
      "Epoch 9/20\n",
      "1/1 - 0s - loss: 0.7654 - categorical_accuracy: 0.9500 - val_loss: 1.0017 - val_categorical_accuracy: 0.8000\n",
      "Epoch 10/20\n",
      "1/1 - 0s - loss: 0.6470 - categorical_accuracy: 0.9500 - val_loss: 0.9313 - val_categorical_accuracy: 0.8060\n",
      "Epoch 11/20\n",
      "1/1 - 0s - loss: 0.5424 - categorical_accuracy: 0.9643 - val_loss: 0.8703 - val_categorical_accuracy: 0.8100\n",
      "Epoch 12/20\n",
      "1/1 - 0s - loss: 0.4734 - categorical_accuracy: 0.9714 - val_loss: 0.8193 - val_categorical_accuracy: 0.8160\n",
      "Epoch 13/20\n",
      "1/1 - 0s - loss: 0.4028 - categorical_accuracy: 0.9786 - val_loss: 0.7794 - val_categorical_accuracy: 0.8160\n",
      "Epoch 14/20\n",
      "1/1 - 0s - loss: 0.3526 - categorical_accuracy: 0.9786 - val_loss: 0.7509 - val_categorical_accuracy: 0.8260\n",
      "Epoch 15/20\n",
      "1/1 - 0s - loss: 0.3112 - categorical_accuracy: 0.9857 - val_loss: 0.7336 - val_categorical_accuracy: 0.8220\n",
      "Epoch 16/20\n",
      "1/1 - 0s - loss: 0.2826 - categorical_accuracy: 0.9786 - val_loss: 0.7257 - val_categorical_accuracy: 0.8180\n",
      "Epoch 17/20\n",
      "1/1 - 0s - loss: 0.2737 - categorical_accuracy: 0.9857 - val_loss: 0.7246 - val_categorical_accuracy: 0.8100\n",
      "Epoch 18/20\n",
      "1/1 - 0s - loss: 0.2308 - categorical_accuracy: 0.9929 - val_loss: 0.7278 - val_categorical_accuracy: 0.8120\n",
      "Epoch 19/20\n",
      "1/1 - 0s - loss: 0.2167 - categorical_accuracy: 0.9929 - val_loss: 0.7322 - val_categorical_accuracy: 0.8200\n",
      "Epoch 20/20\n",
      "1/1 - 0s - loss: 0.2171 - categorical_accuracy: 0.9929 - val_loss: 0.7383 - val_categorical_accuracy: 0.8140\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen, shuffle=False, epochs=20, verbose=2, validation_data=val_gen\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the trained model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.7424\n",
      "\tcategorical_accuracy: 0.7979\n"
     ]
    }
   ],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)\n",
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node and link importance via saliency maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to understand why a selected node is predicted as a certain class we want to find the node feature importance, total node importance, and link importance for nodes and edges in the selected node's neighbourhood (ego-net). These importances give information about the effect of changes in the node's features and its neighbourhood on the prediction of the node, specifically:\n",
    "\n",
    "#### Node feature importance:\n",
    "Given the selected node $t$ and the model's prediction $s(c)$ for class $c$. The feature importance can be calculated for each node $v$ in the selcted node's ego-net where the importance of feature $f$ for node $v$ is the change predicted score $s(c)$ for the selected node when the feature $f$ of node $v$ is perturbed.\n",
    "\n",
    "#### Total node importance:\n",
    "This is defined as the sum of the feature importances for node $v$ for all features. Nodes with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "\n",
    "#### Link importance:\n",
    "This is defined as the change in the selected node's predicted score $s(c)$ if the link $e=(u, v)$ is removed from the graph. Links with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "\n",
    "Node and link importances can be used to assess the role of nodes and links in model's predictions for the node(s) of interest (the selected node). For datasets like CORA-ML, the features and edges are binary, vanilla gradients may not perform well so we use integrated gradients [[1]](#refs) to compute them.\n",
    "\n",
    "Another interesting application of node and link importances is to identify model vulnerabilities to attacks via perturbing node features and graph structure (see [[2]](#refs))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate these importances we use the StellarGraph `saliency_maps` routines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stellargraph.utils.saliency_maps import IntegratedGradients, GradientSaliency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the target node whose prediction is to be interpreted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_idx = 7  # index of the selected node in G.nodes()\n",
    "target_nid = graph_nodes[target_idx]  # Node ID of the selected node\n",
    "y_true = all_targets[target_idx]  # true class of the target node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected node id: 1109199, \n",
      "True label: [0. 1. 0. 0. 0. 0. 0.], \n",
      "Predicted scores: [0.02 0.75 0.   0.   0.21 0.   0.  ]\n"
     ]
    }
   ],
   "source": [
    "all_gen = generator.flow(graph_nodes)\n",
    "y_pred = model.predict_generator(all_gen)[0, target_idx]\n",
    "class_of_interest = np.argmax(y_pred)\n",
    "\n",
    "print(\n",
    "    \"Selected node id: {}, \\nTrue label: {}, \\nPredicted scores: {}\".format(\n",
    "        target_nid, y_true, y_pred.round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node feature importance by using integrated gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_grad_saliency = IntegratedGradients(model, train_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the parameters of `get_node_importance` method, `X` and `A` are the feature and adjacency matrices, respectively. `target_idx` is the node of interest, and `class_of_interest` is set as the predicted label of the node. `steps` indicates the number of steps used to approximate the integration in integrated gradients calculation. A larger value of `steps` gives better approximation, at the cost of higher computational overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "integrated_node_importance = int_grad_saliency.get_node_importance(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "integrated_node_importance [0.09 2.84 0.01 ... 0.   0.   0.  ]\n",
      "integrate_node_importance.shape = (2708,)\n",
      "integrated self-importance of target node 1109199: 6.02\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nintegrated_node_importance\", integrated_node_importance.round(2))\n",
    "print(\"integrate_node_importance.shape = {}\".format(integrated_node_importance.shape))\n",
    "print(\n",
    "    \"integrated self-importance of target node {}: {}\".format(\n",
    "        target_nid, integrated_node_importance[target_idx].round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that number of non-zero node importance values is less or equal the number of nodes in target node's K-hop ego net (where K is the number of GCN layers in the model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ego = nx.ego_graph(Gnx, target_nid, radius=len(gcn.activations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the ego graph: 202\n",
      "Number of non-zero elements in integrated_node_importance: 202\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of nodes in the ego graph: {}\".format(len(G_ego.nodes())))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrated_node_importance: {}\".format(\n",
    "        np.count_nonzero(integrated_node_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now compute the link importance using integrated gradients [1]. Integrated gradients are obtained by cumulating the gradients along the path between the baseline (all-zero graph) and the state of the graph. They provide better sensitivity for the graphs with binary features and edges compared with the vanilla gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrate_link_importance.shape = (2708, 2708)\n"
     ]
    }
   ],
   "source": [
    "integrate_link_importance = int_grad_saliency.get_integrated_link_masks(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")\n",
    "print(\"integrate_link_importance.shape = {}\".format(integrate_link_importance.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some sanity checks:\n",
    "We expect the number of non-zero elements in the integrated link importance be same or less than the number of real edges in the ego graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, _, A), _ = train_gen[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of edges in the ego graph: 210\n",
      "Number of non-zero elements in integrate_link_importance: 210\n"
     ]
    }
   ],
   "source": [
    "# The built-in number_of_edges function for the ego graph does not count the self-loops and some reversed edges\n",
    "# in the non-directed graph so we do the sanity check as the following.\n",
    "G_ego_edges = set()\n",
    "for i in np.nonzero(A[0, target_idx])[0]:\n",
    "    G_ego_edges.add((graph_nodes[target_idx], graph_nodes[i]))\n",
    "    for j in np.nonzero(A[0, i])[0]:\n",
    "        G_ego_edges.add((graph_nodes[i], graph_nodes[j]))\n",
    "print(\"Number of edges in the ego graph: {}\".format(len(G_ego_edges)))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrate_link_importance: {}\".format(\n",
    "        np.count_nonzero(integrate_link_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now find the nodes that have the highest importance to the prediction of the selected node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most important links by integrated gradients are:\n",
      " [(6214, 6378), (6214, 6196), (6214, 149669), (6214, 28278), (6214, 13960), (6214, 345340), (6214, 1105764), (6214, 95589), (6214, 124064), (1109199, 6214)]\n"
     ]
    }
   ],
   "source": [
    "sorted_indices = np.argsort(integrate_link_importance.flatten())\n",
    "N = len(graph_nodes)\n",
    "integrated_link_importance_rank = [\n",
    "    (graph_nodes[k // N], graph_nodes[k % N]) for k in sorted_indices[::-1]\n",
    "]\n",
    "topk = 10\n",
    "print(\n",
    "    \"Top {} most important links by integrated gradients are:\\n {}\".format(\n",
    "        topk, integrated_link_importance_rank[-topk:]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the labels as an attribute for the nodes in the graph. The labels are used to color the nodes in different classes.\n",
    "nx.set_node_attributes(\n",
    "    G_ego, values={x[0]: {\"subject\": x[1]} for x in node_data[\"subject\"].items()}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we plot the link and node importance (computed by integrated gradients) of the nodes within the ego graph of the target node. \n",
    "\n",
    "For nodes, the shape of the node indicates the positive/negative importance the node has. 'round' nodes have positive importance while 'diamond' nodes have negative importance. The size of the node indicates the value of the importance, e.g., a large diamond node has higher negative importance. \n",
    "\n",
    "For links, the color of the link indicates the positive/negative importance the link has. 'red' links have positive importance while 'blue' links have negative importance. The width of the link indicates the value of the importance, e.g., a thicker blue link has higher negative importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.52887611756887"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1441278339456767"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate_link_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_size_factor = 1e2\n",
    "link_width_factor = 2\n",
    "\n",
    "nodes = list(G_ego.nodes())\n",
    "colors = pd.DataFrame(\n",
    "    [v[1][\"subject\"] for v in G_ego.nodes(data=True)], index=nodes, columns=[\"subject\"]\n",
    ")\n",
    "colors = np.argmax(target_encoding.transform(colors.to_dict(\"records\")), axis=1) + 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "pos = nx.spring_layout(G_ego)\n",
    "\n",
    "# Draw ego as large and red\n",
    "node_sizes = [integrated_node_importance[graph_nodes.index(k)] for k in nodes]\n",
    "node_shapes = [\"o\" if w > 0 else \"d\" for w in node_sizes]\n",
    "\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "node_size_scale = node_size_factor / np.max(node_sizes)\n",
    "for k in range(len(nodes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(nodes[k])\n",
    "        positive_node_sizes.append(node_size_scale * node_sizes[k])\n",
    "\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(nodes[k])\n",
    "        negative_node_sizes.append(node_size_scale * abs(node_sizes[k]))\n",
    "\n",
    "# Plot the ego network with the node importances\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_nid)]),\n",
    "    node_shape=\"*\",\n",
    "    node_color=[colors[nodes.index(target_nid)]],\n",
    "    cmap=cmap,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "\n",
    "# Draw the edges with the edge importances\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    integrate_link_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then remove the node or edge in the ego graph one by one and check how the prediction changes. By doing so, we can obtain the ground truth importance of the nodes and edges. Comparing the following figure and the above one can show the effectiveness of integrated gradients as the importance approximations are relatively consistent with the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_bk = deepcopy(X)\n",
    "A_bk = deepcopy(A)\n",
    "selected_nodes = np.array([[target_idx]], dtype=\"int32\")\n",
    "nodes = [graph_nodes.index(v) for v in G_ego.nodes()]\n",
    "edges = [(graph_nodes.index(u), graph_nodes.index(v)) for u, v in G_ego.edges()]\n",
    "\n",
    "clean_prediction = model.predict([X, selected_nodes, A]).squeeze()\n",
    "predict_label = np.argmax(clean_prediction)\n",
    "\n",
    "groud_truth_node_importance = np.zeros((N,))\n",
    "for node in nodes:\n",
    "    # we set all the features of the node to zero to check the ground truth node importance.\n",
    "    X_perturb = deepcopy(X_bk)\n",
    "    X_perturb[:, node, :] = 0\n",
    "    predict_after_perturb = model.predict([X_perturb, selected_nodes, A]).squeeze()\n",
    "    groud_truth_node_importance[node] = (\n",
    "        clean_prediction[predict_label] - predict_after_perturb[predict_label]\n",
    "    )\n",
    "\n",
    "node_shapes = [\n",
    "    \"o\" if groud_truth_node_importance[k] > 0 else \"d\" for k in range(len(nodes))\n",
    "]\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "# node_size_scale is used for better visulization of nodes\n",
    "node_size_scale = node_size_factor / max(groud_truth_node_importance)\n",
    "\n",
    "for k in range(len(node_shapes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(graph_nodes[nodes[k]])\n",
    "        positive_node_sizes.append(\n",
    "            node_size_scale * groud_truth_node_importance[nodes[k]]\n",
    "        )\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(graph_nodes[nodes[k]])\n",
    "        negative_node_sizes.append(\n",
    "            node_size_scale * abs(groud_truth_node_importance[nodes[k]])\n",
    "        )\n",
    "X = deepcopy(X_bk)\n",
    "groud_truth_edge_importance = np.zeros((N, N))\n",
    "for edge in edges:\n",
    "    A = deepcopy(A_bk)\n",
    "    if A[0, edge[0], edge[1]] == 0:\n",
    "        continue\n",
    "    # we set the weight of a given edge to zero to check the ground truth link importance\n",
    "    A[:, edge[0], edge[1]] = 0\n",
    "    predict_after_perturb = model.predict([X, selected_nodes, A]).squeeze()\n",
    "    groud_truth_edge_importance[edge[0], edge[1]] = (\n",
    "        predict_after_perturb[predict_label] - clean_prediction[predict_label]\n",
    "    ) / (0 - 1)\n",
    "    A[:, edge[0], edge[1]] = 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_idx)]),\n",
    "    node_color=[colors[nodes.index(target_idx)]],\n",
    "    cmap=cmap,\n",
    "    node_shape=\"*\",\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "# Draw the ego net\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    groud_truth_edge_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By comparing the above two figures, one can see that the integrated gradients are quite consistent with the brute-force approach. The main benefit of using integrated gradients is scalability. The gradient operations are very efficient to compute on deep learning frameworks with the parallism provided by GPUs. Also, integrated gradients can give the importance of individual node features, for all nodes in the graph. Achieving this by brute-force approch is often non-trivial. "
   ]
  }
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